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NVIDIA AI Infrastructure Sample Questions (Q207-Q212):

NEW QUESTION # 207
You're designing a new InfiniBand network for a distributed deep learning workload. The workload consists of a mix of large-message all- to-all communication and small-message parameter synchronization. Considering the different traffic patterns, what routing strategy would MOST effectively minimize latency and maximize bandwidth utilization across the fabric?

A. Rely solely on the default Subnet Manager (SM) with a Min Hop path selection algorithm.
B. Disable multicast.
C. Implement a purely deterministic routing scheme, disabling all adaptive routing features.
D. Utilize a combination of Adaptive Routing (AR) to handle dynamic traffic patterns and Quality of Service (QOS) to prioritize small-message parameter synchronization.
E. Implement a static routing scheme with manually configured forwarding tables on each switch.

Answer: D

Explanation:
A combination of AR and QOS provides the most flexible and effective solution. AR can dynamically adapt to changing traffic patterns and congestion, optimizing for large-message all-to-all communication. QOS can prioritize small-message parameter synchronization, minimizing latency for critical control traffic. Min Hop routing may not always choose the optimal paths, especially in complex topologies. Static routing is difficult to manage and doesn't adapt to changing network conditions. Disabling AR can lead to congestion.

NEW QUESTION # 208
You are tasked with diagnosing performance issues on a GPU server running a large-scale HPC simulation. The simulation utilizes multiple GPUs and InfiniBand for inter-GPU communication. You suspect that RDMA (Remote Direct Memory Access) is not functioning correctly. How would you comprehensively test and verify the proper operation of RDMA between the GPUs?

A. Run 'nvidia-smi topo -m' to check the GPU interconnect topology and verify that NVLink or PCle is being used for communication.
B. Employ and from the 'perftest' suite to measure RDMA bandwidth and latency between GPUs.
C. Utilize NCCL's internal diagnostic tools to verify proper inter-GPU communication within the simulation.
D. Use 'ping' to verify basic network connectivity between the server's InfiniBand interfaces.
E. Monitor CPU utilization during the simulation; high CPU usage suggests that RDMA is not offloading communication effectively.

Answer: A,B,C

Explanation:
(A) and (B) directly measure RDMA performance. 'nvidia-smi topo -m' (C) verifies the physical interconnect. NCCL diagnostic tools (D) confirm application-level communication. 'ping' (A) only tests basic network connectivity, not RDMA functionality. While high CPU usage (E) can indicate RDMA issues, it's an indirect symptom, not a direct test.

NEW QUESTION # 209
You have a deep learning application that requires a specific version of the CUDA toolkit inside the container. How should you best ensure that the correct CUDA version is available within the container, considering the NVIDIA Container Toolkit is installed on the host?

A. Install the required CUDA toolkit version directly on the host operating system. The NVIDIA Container Toolkit will automatically map it into the container.
B. Specify the desired CUDA version when running the container using the '-env flag. The NVIDIA Container Toolkit will dynamically install the CUDA version during container startup.
C. Use a base image (e.g., from NVIDIA NGC) that already includes the desired CUDA toolkit version. This approach provides a consistent and reproducible environment.
D. Manually copy the necessary CUDA libraries from the host into the container using 'docker cp' before running the application.
E. Use the nvidia-container-cli to modify the existing image to install the proper cuda version.

Answer: C

Explanation:
The recommended approach is to use a base image that already contains the desired CUDA version. NVIDIA provides pre-built images on NGC (NVIDIA GPU Cloud) that are specifically designed for deep learning and include the appropriate CUDA versions and other dependencies. Installing CUDA on the host and expecting it to be magically mapped (A) is not reliable. The NVIDIA Container Toolkit doesn't install CUDA on the fly (B). Manually copying libraries (D) is error-prone and doesn't handle dependencies well. While technically possible, using nvidia- container-cli to modify the image is more complex than using a base image.

NEW QUESTION # 210
You need to verify the NVLink connectivity between GPUs in a DGX server. Which command-line utility is the MOST reliable and provides detailed NVLink status?

A. nvidia-smi
B. Ispci
C. gpustat
D. dcgmi diag -t 1004
E. nvlink_info (Hypothetical command)

Answer: D

Explanation:
'dcgmi diag -t 1004' is the correct command. 'nvidia-smi' provides basic GPIJ information, but 'dcgmi diag -t 1004' (part of the Data Center GPU Manager) provides specific diagnostic tests for NVLink connectivity. 'Ispci' lists PCle devices, not specifically NVLink. 'gpustat' is a monitoring tool. 'nvlink_info' is hypothetical.

NEW QUESTION # 211
You are designing a storage solution for a new AI inference cluster that requires extremely low latency for model serving. Which storage technology and configuration would be MOST suitable to meet this stringent latency requirement?

A. Amazon S3 object storage accessed over a high-bandwidth internet connection.
B. A distributed file system deployed on spinning HDDs with a large read-ahead cache.
C. A traditional Fibre Channel SAN with a dedicated storage array.
D. NVMe-oF (NVMe over Fabrics) using RDMA over Converged Ethernet (RoCE) connected to a cluster of NVMe drives.
E. A software-defined storage (SDS) solution running on commodity hardware with SATA SSDs.

Answer: D

Explanation:
NVMe-oF with RoCE (option B) provides the lowest latency due to the combination of NVMe's low-latency access to flash storage and RDMA's ability to bypass the CPU for data transfer. HDDs (A) are too slow. SATA SSDs (C) are slower than NVMe. Object storage (D) introduces network latency. Fibre Channel (E) is a viable option, but NVMe-oF typically offers lower latency and greater flexibility.

NEW QUESTION # 212
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Free PDF Quiz 2025 NVIDIA NCP-AII: NVIDIA AI Infrastructure Perfect Reliable Exam Topics

Enhance Your Exam Performance With NVIDIA NCP-AII Web-Based Practice Test

NVIDIA AI Infrastructure Sample Questions (Q207-Q212):

Free PDF NCP-AII - NVIDIA AI Infrastructure Newest Reliable Exam Topics

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